PMID- 31660883
OWN - NLM
STAT- MEDLINE
DCOM- 20191230
LR  - 20200108
IS  - 1471-2334 (Electronic)
IS  - 1471-2334 (Linking)
VI  - 19
IP  - 1
DP  - 2019 Oct 28
TI  - Bayesian modeling of spatiotemporal patterns of TB-HIV co-infection risk in 
      Kenya.
PG  - 902
LID - 10.1186/s12879-019-4540-z [doi]
LID - 902
AB  - BACKGROUND: Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) diseases are 
      globally acknowledged as a public health challenge that exhibits adverse 
      bidirectional relations due to the co-epidemic overlap. To understand the 
      co-infection burden we used the case notification data to generate spatiotemporal 
      maps that described the distribution and exposure hypotheses for further 
      epidemiologic investigations in areas with unusual case notification levels. 
      METHODS: We analyzed the TB and TB-HIV case notification data from the Kenya 
      national TB control program aggregated for forty-seven counties over a seven-year 
      period (2012-2018). Using spatiotemporal poisson regression models within the 
      Integrated Nested Laplace Approach (INLA) paradygm, we modeled the risk of TB-HIV 
      co-infection. Six competing models with varying space-time formulations were 
      compared to determine the best fit model. We then assessed the geographic 
      patterns and temporal trends of coinfection risk by mapping the posterior 
      marginal from the best fit model. RESULTS: Of the total 608,312 TB case 
      notifications, 194,129 were HIV co-infected. The proportion of TB-HIV 
      co-infection was higher in females (39.7%) than in males (27.0%). A significant 
      share of the co-infection was among adults aged 35 to 44 years (46.7%) and 45 to 
      54 years (42.1%). Based on the Bayesian Defiance Information (DIC) and the 
      effective number of parameters (pD) comparisons, the spatiotemporal model 
      allowing space-time interaction was the best in explaining the geographical 
      variations in TB-HIV coinfection. The model results suggested that the risk of 
      TB-HIV coinfection was influenced by infrastructure index (Relative risk 
      (RR) = 5.75, Credible Interval (Cr.I) = (1.65, 19.89)) and gender ratio 
      (RR = 5.81e(-04), Cr. I = (1.06e(-04), 3.18e(-03)). The lowest and highest 
      temporal relative risks were in the years 2016 at 0.9 and 2012 at 1.07 
      respectively. The spatial pattern presented an increased co-infection risk in a 
      number of counties. For the spatiotemporal interaction, only a few counties had a 
      relative risk greater than 1 that varied in different years. CONCLUSIONS: We 
      identified elevated risk areas for TB/HIV co-infection and fluctuating temporal 
      trends which could be because of improved TB case detection or surveillance bias 
      caused by spatial heterogeneity in the co-infection dynamics. Focused 
      interventions and continuous TB-HIV surveillance will ensure adequate resource 
      allocation and significant reduction of HIV burden amongst TB patients.
FAU - Otiende, Verrah
AU  - Otiende V
AUID- ORCID: 0000-0001-6147-3547
AD  - Department of Mathematical Sciences, Pan African University Institute of Basic 
      Sciences Technology and Innovation, Nairobi, Kenya. 
      otiende.verrah@students.jkuat.ac.ke.
FAU - Achia, Thomas
AU  - Achia T
AD  - School of Mathematics, Statistics & Computer Science, University of 
      KwaZulu-Natal, Pietermaritzburg, South Africa.
FAU - Mwambi, Henry
AU  - Mwambi H
AD  - School of Mathematics, Statistics & Computer Science, University of 
      KwaZulu-Natal, Pietermaritzburg, South Africa.
LA  - eng
GR  - PhD Scholarship/African Union/
PT  - Journal Article
DEP - 20191028
PL  - England
TA  - BMC Infect Dis
JT  - BMC infectious diseases
JID - 100968551
SB  - IM
MH  - Adolescent
MH  - Adult
MH  - Age Factors
MH  - *Bayes Theorem
MH  - Coinfection/*epidemiology
MH  - *Epidemics
MH  - Female
MH  - HIV Infections/*epidemiology
MH  - Humans
MH  - Kenya/epidemiology
MH  - Male
MH  - Middle Aged
MH  - Poverty
MH  - Public Health
MH  - Public Health Surveillance
MH  - Risk
MH  - Sex Factors
MH  - Tuberculosis/*epidemiology
MH  - Young Adult
PMC - PMC6819548
OTO - NOTNLM
OT  - Bayesian modeling
OT  - Kenya
OT  - TB-HIV co-infection
OT  - co-epidemic burden
COIS- The authors declare that they have no competing interests.
EDAT- 2019/10/30 06:00
MHDA- 2019/12/31 06:00
PMCR- 2019/10/28
CRDT- 2019/10/30 06:00
PHST- 2019/04/25 00:00 [received]
PHST- 2019/10/09 00:00 [accepted]
PHST- 2019/10/30 06:00 [entrez]
PHST- 2019/10/30 06:00 [pubmed]
PHST- 2019/12/31 06:00 [medline]
PHST- 2019/10/28 00:00 [pmc-release]
AID - 10.1186/s12879-019-4540-z [pii]
AID - 4540 [pii]
AID - 10.1186/s12879-019-4540-z [doi]
PST - epublish
SO  - BMC Infect Dis. 2019 Oct 28;19(1):902. doi: 10.1186/s12879-019-4540-z.